Update Readme

README.md

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 # Model Card for RootSignals-Judge-Llama-70B
 
-Root Judge is a powerful mid-sized model that enables reliable and customizable LLM system evaluations. 
-Root Judge was post-trained from Llama-3.3-70B-Instruct on a high quality, human-annotated dataset mix for pairwise preference choice judgments and multi-turn instruction following with source citing.
-The model weights are freely made available in FP8 to facilitate cost effective research and application use.
+**Root Judge** is a powerful mid-sized model that enables reliable and customizable LLM system evaluations. 
+Root Judge was post-trained from *Llama-3.3-70B-Instruct* on a high quality, human-annotated dataset mix for pairwise preference choice judgments and multi-turn instruction following with source citing.
+The model weights are freely available in FP8 to facilitate cost effective research as well as commercial use.
 
 Root Judge’s performance surpasses the Llama-3.3-Instruct model and similar sized open models on Instruction following and 
 achieves SOTA on hallucination detection compared to leading closed models, at a fraction of the cost. 
@@ -107,9 +107,9 @@ while also slightly outperforming it on public instruction following benchmarks
 - **Language(s) (NLP):** Primarily English
 - **Finetuned from model:** meta-llama/Llama-3.3-70B-Instruct
 
-## How to Get Started with the Model
+## Getting Started
 
-We recommend using SGLang for production use together with xml tags for important sections in your prompt. At least 96GB of VRAM is recommended. 
+We recommend using [SGLang](https://github.com/sgl-project/sglang) for production use together with *xml tags* for important sections in your prompt. At least 96GB of VRAM is recommended. 
 While the model runs on 80GB VRAM the effective context size (around 7k total tokens) will be too low for evaluating most RAG inputs.
 
 SGlang example for a single Nvidia H100 (80GB):